What you'll learn:
- Learn the theory and implement optimization algorithms from scratch for solving real problems
- Implement step by step the following algorithms in Python: random search, hill climb, simulated annealing, and genetic algorithms
- Solve real problems for optimizing flight calendars and dormitory room optimization (limited resources)
- Implement optimization algorithms using predefined libraries
What would an “optimal world” look like to you? Would people get along better? Would transport run faster? Would we take better care of our environment?
Many data scientists choose to optimize by using pre-built machine learning libraries. But we think that this kind of 'plug-and-play' study hinders your learning. That's why this course gets you to build an optimization algorithm from the ground up.
In Artificial Intelligence: Optimization Algorithms in Python, you'll get to learn all the logic and math behind optimization algorithms. With two highly practical case studies, you'll also find out how to apply them to solve real-world problems.
In the first case study, we'll optimize travel plans for six friends who want to fly out from the same airport. In the second case study, we'll optimize the way university administrators allocate dorm rooms to new students.
On the way, we'll learn what optimization algorithms are. We'll find out how they can be applied to daily business practice. And we'll see how they can learn by themselves.
This course introduces you to four types of optimization algorithms:
- random search
- hill climb
- simulated annealing, and
- genetic
Don't worry if you're not yet sure what any of these are. We'll go through each one in detail, and you'll find out how to build each of them in our two case studies."
